The present invention relates to anatomical object detection in medical image data, and more particularly, to anatomical object detection in medical image data using deep neural networks.
Fast and robust anatomical object detection is a fundamental task in medical image analysis that supports the entire clinical imaging workflow from diagnosis, patient stratification, therapy planning, intervention, and follow-up. Automatic detection of an anatomical object is a prerequisite for many medical image analysis tasks, such as segmentation, motion tracking, and disease diagnosis and quantification. Marginal space learning (MSL) was previously introduced to address the problem of anatomy detection and tracking in medical images, such as computed tomography (CT), magnetic resonance (MR), ultrasound, and fluoroscopic images. MSL is an efficient discriminative learning framework that typically uses handcrafted image features extracted from training images to train discriminative classifiers for anatomical object detection in a supervised manner. MSL works well for detecting anatomical structures in various two-dimensional (2D) and three-dimensional (3D) medical imaging modalities. However, anatomical object detection using MSL is not always robust, especially for some challenging detection problems in which the anatomical objects exhibit large variations in anatomy, shape, or appearance in the medical images